| 2,209 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
8.8360101e-05 |
| 3,479 |
LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans |
2023 |
VLDB |
7.2665349e-05 |
| 3,565 |
Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift |
2023 |
SIGMOD |
7.200937e-05 |
| 4,191 |
Kepler: Robust Learning for Faster Parametric Query Optimization |
2023 |
SIGMOD |
6.7425275e-05 |
| 4,240 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
6.7064546e-05 |
| 4,677 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
6.4721041e-05 |
| 5,216 |
Stage: Query Execution Time Prediction in Amazon Redshift |
2024 |
SIGMOD |
6.2218868e-05 |
| 5,236 |
FASTgres: Making Learned Query Optimizer Hinting Effective |
2023 |
VLDB |
6.2153504e-05 |
| 5,316 |
How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks |
2025 |
SIGMOD |
6.1827415e-05 |
| 5,438 |
Eraser: Eliminating Performance Regression on Learned Query Optimizer |
2024 |
VLDB |
6.1278045e-05 |
| 5,630 |
Sample-Efficient Cardinality Estimation Using Geometric Deep Learning |
2024 |
VLDB |
6.056758e-05 |
| 5,700 |
PilotScope: Steering Databases with Machine Learning Drivers |
2024 |
VLDB |
6.028998e-05 |
| 5,776 |
Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries |
2023 |
SIGMOD |
5.9957692e-05 |
| 6,298 |
Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective |
2024 |
VLDB |
5.8177684e-05 |
| 6,575 |
Can Large Language Models Be Query Optimizer for Relational Databases? |
2026 |
SIGMOD |
5.7428777e-05 |
| 6,636 |
Join Order Selection with Deep Reinforcement Learning: Fundamentals, Techniques, and Challenges |
2023 |
VLDB |
5.7243042e-05 |
| 6,664 |
Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation |
2023 |
SIGMOD |
5.715134e-05 |
| 6,714 |
Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis |
2023 |
VLDB |
5.6993812e-05 |
| 7,021 |
Rethinking Learned Cost Models: Why Start from Scratch? |
2023 |
SIGMOD |
5.6168049e-05 |
| 7,072 |
E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model |
2025 |
VLDB |
5.6053987e-05 |
| 7,464 |
T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees |
2025 |
SIGMOD |
5.5189616e-05 |
| 7,546 |
Learned Offline Query Planning via Bayesian Optimization |
2025 |
SIGMOD |
5.4966669e-05 |
| 7,929 |
SlabCity: Whole-Query Optimization using Program Synthesis |
2023 |
VLDB |
5.4231855e-05 |
| 7,981 |
The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions |
2024 |
VLDB |
5.4117272e-05 |
| 8,373 |
GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints |
2026 |
SIGMOD |
5.3427119e-05 |
| 8,638 |
LIMAO: A Framework for Lifelong Modular Learned Query Optimization |
2025 |
VLDB |
5.2965922e-05 |
| 8,969 |
Conformal Prediction for Verifiable Learned Query Optimization |
2025 |
VLDB |
5.2477982e-05 |
| 9,134 |
Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems |
2024 |
VLDB |
5.2223611e-05 |
| 9,214 |
BASE: Bridging the Gap between Cost and Latency for Query Optimization |
2023 |
VLDB |
5.2054849e-05 |
| 9,556 |
Athena: An Effective Learning-based Framework for Query Optimizer Performance Improvement |
2025 |
SIGMOD |
5.1580326e-05 |
| 9,635 |
Low Rank Learning for Offline Query Optimization |
2025 |
SIGMOD |
5.1453041e-05 |
| 9,663 |
BladeDISC: Optimizing Dynamic Shape Machine Learning Workloads via Compiler Approach |
2023 |
SIGMOD |
5.142891e-05 |
| 9,725 |
APQO: An Adaptive Framework for Parametric Query Optimization |
2026 |
SIGMOD |
5.1325223e-05 |
| 9,804 |
NeuSO: Neural Optimizer for Subgraph Queries |
2026 |
SIGMOD |
5.1233734e-05 |
| 9,900 |
Approximate Sketches |
2024 |
SIGMOD |
5.1110481e-05 |
| 9,962 |
Graph Transformers for Query Plan Representation: Potentials and Challenges |
2025 |
VLDB |
5.1014161e-05 |
| 10,152 |
Improving DBMS Scheduling Decisions with Accurate Performance Prediction on Concurrent Queries |
2025 |
VLDB |
5.0691578e-05 |
| 10,291 |
Towards Full Stack Adaptivity in Permissioned Blockchains |
2024 |
VLDB |
5.042478e-05 |
| 10,320 |
veDB-HTAP: a Highly Integrated, Efficient and Adaptive HTAP System |
2025 |
VLDB |
5.0362412e-05 |
| 10,343 |
An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL |
2025 |
SIGMOD |
5.0176429e-05 |
| 10,424 |
Are Learned DBMS Components Robust to Workload Drift?: [Experiments & Analysis] |
2026 |
SIGMOD |
4.9769913e-05 |
| 10,495 |
NeurBench: A Benchmark Suite for Learned Database Components with Drift Modeling: [Experiments & Analysis] |
2026 |
SIGMOD |
4.9769913e-05 |
| 10,500 |
On Self-Designing Learned Indexes |
2026 |
SIGMOD |
4.9769913e-05 |
| 10,519 |
Succinct Structure Representations for Efficient Query Optimization |
2026 |
SIGMOD |
4.9769913e-05 |
| 10,543 |
Rainbow: Risk-aware Index Benefit Estimation Facing Out Of Distribution Workloads |
2026 |
SIGMOD |
4.9769913e-05 |
| 10,607 |
SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer |
2026 |
SIGMOD |
4.9769913e-05 |
| 10,644 |
Divo: Learning a Stable and Effective Query Optimizer with a Diverse Workload |
2026 |
SIGMOD |
4.9769913e-05 |
| 10,703 |
Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking |
2026 |
SIGMOD |
4.9769913e-05 |
| 10,708 |
LIO: A lightweight and interpretable query optimizer based on an evolutionary forest |
2026 |
VLDB |
4.9769913e-05 |
| 10,710 |
Sample-based Distinct Cardinality Estimation for Multiple Attributes in Multi-Dataset Queries |
2026 |
VLDB |
4.9769913e-05 |